[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100576903":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":10,"centralContacts":24,"locations":30,"responsibleParty":73,"collaborators":10,"id":78,"slug":79,"hasResults":80,"nctId":81,"briefTitle":82,"officialTitle":83,"acronym":84,"eligibilityCriteria":85,"healthyVolunteers":86,"sex":87,"minAge":88,"maxAge":89,"enrollmentInfo":90,"targetDuration":10,"studyType":93,"phases":10,"briefSummary":94,"conditions":95,"keywords":97,"overallStatus":33,"whyStopped":10,"lastUpdateSubmitDate":101,"lastUpdatePostDateStruct":102,"startDateStruct":105,"completionDateStruct":107,"leadSponsor":109,"locationsCount":110},{"fullName":5,"class":6},"The Eye Hospital of Wenzhou Medical University","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Healthy Maternal and Neonatal Cohort",null,"This group consists of pregnant mothers with no pregnancy-related diseases and their healthy newborns. Participants in this cohort will serve as the control group for comparison to the experimental group. No interventions or treatments will be administered to this cohort, as they represent the baseline of healthy pregnancies and newborns.",[13],"Diagnostic Test: AI-Based Diagnostic and Prognostic Model",{"label":15,"type":10,"description":16,"interventionNames":17},"Maternal and Neonatal Disease Cohort","This group consists of pregnant mothers who have been diagnosed with pregnancy-related diseases or their affected newborns. Participants in this cohort will serve as the experimental group for evaluating the effectiveness of the early prediction model in identifying maternal and neonatal health risks.",[13],[19],{"type":20,"name":21,"description":22,"armGroupLabels":23,"otherNames":10},"DIAGNOSTIC_TEST","AI-Based Diagnostic and Prognostic Model","This intervention involves an AI system that integrates multimodal data, including maternal health records, laboratory test results, and imaging data, to predict the risk of maternal and neonatal diseases. The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of health complications. By analyzing historical health data, the model aims to predict potential risks for both mothers and infants, improving early intervention and outcomes.",[9,15],[25],{"name":26,"role":27,"phone":28,"phoneExt":10,"email":29},"Fei Liu, MD","CONTACT","+86 13810512704","liufei_2359@163.com",[31,49,63],{"facility":32,"status":33,"city":34,"state":35,"zip":10,"country":36,"countryCode":37,"cosmosGeoPoint":38,"geoPoint":43,"contacts":44},"Guangzhou Women and Children's Medical Center","RECRUITING","Guangzhou","Guangdong","China","CN",{"type":39,"coordinates":40},"Point",[41,42],113.25,23.11667,{"lat":42,"lon":41},[45],{"name":46,"role":27,"phone":47,"phoneExt":10,"email":48},"Bingzhou Liu, MD","+86-0756-2222569","mr_jerry_99@163.com",{"facility":50,"status":33,"city":51,"state":52,"zip":10,"country":36,"countryCode":37,"cosmosGeoPoint":53,"geoPoint":57,"contacts":58},"First Affiliated Hospital of Wenzhou Medical University","Wenzhou","Zhejiang",{"type":39,"coordinates":54},[55,56],120.66682,27.99942,{"lat":56,"lon":55},[59],{"name":60,"role":27,"phone":61,"phoneExt":10,"email":62},"Cheng Tang, MD","+86-0577-55579999","c249325687@163.com",{"facility":64,"status":33,"city":51,"state":52,"zip":10,"country":36,"countryCode":37,"cosmosGeoPoint":65,"geoPoint":67,"contacts":68},"Second Affiliated Hospital of Wenzhou Medical University",{"type":39,"coordinates":66},[55,56],{"lat":56,"lon":55},[69],{"name":70,"role":27,"phone":71,"phoneExt":10,"email":72},"Sian Liu, MD","+86-0577-88002888","liusan@mail3.sysu.edu.cn",{"type":74,"investigatorFullName":75,"investigatorTitle":76,"investigatorAffiliation":77,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Kang Zhang","Chief Scientist","Wenzhou Medical University","100576903","early-diagnosis-and-prediction-of-maternal-and-neonatal-diseases-100576903",false,"NCT06791343","Early Diagnosis and Prediction of Maternal and Neonatal Diseases:","Early Prediction and Diagnosis of Maternal and Neonatal Diseases Using Multimodal Health Data","EDPMND","Inclusion Criteria:\n\n1. Pregnant women aged 18 to 45 years.\n2. Women who have received prenatal care at participating centers (e.g., hospitals or clinics).\n3. Availability of comprehensive electronic health records, including prenatal care data, laboratory results, and imaging records.\n4. Willingness to provide consent for participation in the study and the use of historical health data for analysis.\n\nExclusion Criteria:\n\n1. Women under 18 or over 45 years old.\n2. Participants with insufficient follow-up data or missing critical clinical information required for predictive modeling.",true,"ALL","18 Years","45 Years",{"count":91,"type":92},1000000,"ESTIMATED","OBSERVATIONAL","This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying maternal and neonatal diseases, leveraging multimodal health data.",[96],"Pregnancy-Related and Neonatal Disorders",[98,99,100],"Maternal and Neonatal Health","Early Disease Prediction","AI-Assisted Diagnosis","2025-04-16",{"date":103,"type":104},"2025-04-17","ACTUAL",{"date":106,"type":104},"2023-08-01",{"date":108,"type":92},"2025-05",{"name":5,"class":6},3]